Abstract
Support vector regressions (SVR) have been applied to time series prediction recently and perform better than RBF networks. However, only one kernel scale is used in SVR. We implemented a multi scale support vector regression (MS-SVR), which has several different kernel scales, and tested it on two time series benchmarks: Mackey-Glass time series and Laser generated data. In both cases, MS-SVR improves the performance of SVR greatly: fewer support vectors and less prediction error.
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Zheng, D., Wang, J., Zhao, Y. (2006). Time Series Predictions Using Multi-scale Support Vector Regressions. In: Cai, JY., Cooper, S.B., Li, A. (eds) Theory and Applications of Models of Computation. TAMC 2006. Lecture Notes in Computer Science, vol 3959. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11750321_45
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DOI: https://doi.org/10.1007/11750321_45
Publisher Name: Springer, Berlin, Heidelberg
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